Overview

Brought to you by YData

Dataset statistics

Number of variables	23
Number of observations	32588
Missing cells	44028
Missing cells (%)	5.9%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	40.8 MiB
Average record size in memory	1.3 KiB

Variable types

Numeric	6
Text	9
DateTime	2
Categorical	4
URL	2

Alerts

`highest_market_value_in_eur` is highly overall correlated with `market_value_in_eur`	High correlation
`last_season` is highly overall correlated with `market_value_in_eur` and 1 other fields	High correlation
`market_value_in_eur` is highly overall correlated with `highest_market_value_in_eur` and 1 other fields	High correlation
`player_id` is highly overall correlated with `last_season`	High correlation
`position` is highly overall correlated with `sub_position`	High correlation
`sub_position` is highly overall correlated with `position`	High correlation
`first_name` has 2062 (6.3%) missing values	Missing
`country_of_birth` has 2809 (8.6%) missing values	Missing
`city_of_birth` has 2465 (7.6%) missing values	Missing
`country_of_citizenship` has 383 (1.2%) missing values	Missing
`foot` has 2540 (7.8%) missing values	Missing
`height_in_cm` has 2263 (6.9%) missing values	Missing
`contract_expiration_date` has 12098 (37.1%) missing values	Missing
`agent_name` has 16052 (49.3%) missing values	Missing
`market_value_in_eur` has 1564 (4.8%) missing values	Missing
`highest_market_value_in_eur` has 1564 (4.8%) missing values	Missing
`player_id` has unique values	Unique
`url` has unique values	Unique

Reproduction

Analysis started	2025-03-12 19:12:36.842620
Analysis finished	2025-03-12 19:12:42.554519
Duration	5.71 seconds
Software version	ydata-profiling vv4.12.2
Download configuration	config.json

player_id
Real number (ℝ)

High correlation Unique

Distinct	32588
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	347978.22

Minimum	10
Maximum	1380876
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	10
5-th percentile	19354.7
Q1	107936.5
median	283615
Q3	529667
95-th percentile	924831.85
Maximum	1380876
Range	1380866
Interquartile range (IQR)	421730.5

Descriptive statistics

Standard deviation	284104.04
Coefficient of variation (CV)	0.81644203
Kurtosis	0.072423893
Mean	347978.22
Median Absolute Deviation (MAD)	197606
Skewness	0.88322325
Sum	1.1339914 × 10¹⁰
Variance	8.0715108 × 10¹⁰
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10	1	< 0.1%
420921	1	< 0.1%
421726	1	< 0.1%
421710	1	< 0.1%
421637	1	< 0.1%
421636	1	< 0.1%
421424	1	< 0.1%
421319	1	< 0.1%
421284	1	< 0.1%
421224	1	< 0.1%
Other values (32578)	32578	> 99.9%

Value	Count	Frequency (%)
10	1	< 0.1%
26	1	< 0.1%
65	1	< 0.1%
77	1	< 0.1%
80	1	< 0.1%
109	1	< 0.1%
123	1	< 0.1%
132	1	< 0.1%
162	1	< 0.1%
215	1	< 0.1%

Value	Count	Frequency (%)
1380876	1	< 0.1%
1380311	1	< 0.1%
1378362	1	< 0.1%
1375876	1	< 0.1%
1369057	1	< 0.1%
1367128	1	< 0.1%
1365020	1	< 0.1%
1364960	1	< 0.1%
1358448	1	< 0.1%
1358447	1	< 0.1%

first_name
Text

Missing

Distinct	7024
Distinct (%)	23.0%
Missing	2062
Missing (%)	6.3%
Memory size	2.0 MiB

Length

Max length	19
Median length	17
Mean length	5.9755618
Min length	2

Characters and Unicode

Total characters	182410
Distinct characters	105
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4445 ?
Unique (%)	14.6%

Sample

1st row	Miroslav
2nd row	Roman
3rd row	Dimitar
4th row	Tom
5th row	Christoph

Value	Count	Frequency (%)
david	219	0.7%
ivan	149	0.5%
daniel	134	0.4%
marco	133	0.4%
georgios	126	0.4%
aleksandr	123	0.4%
lucas	122	0.4%
luca	117	0.4%
thomas	115	0.4%
michael	114	0.4%
Other values (6765)	29715	95.6%

Most occurring characters

Value	Count	Frequency (%)
a	20379	11.2%
i	15063	8.3%
n	13668	7.5%
e	13659	7.5%
o	12554	6.9%
r	11912	6.5%
l	8680	4.8%
s	8383	4.6%
t	5826	3.2%
m	5017	2.8%
Other values (95)	67269	36.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	182410	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	20379	11.2%
i	15063	8.3%
n	13668	7.5%
e	13659	7.5%
o	12554	6.9%
r	11912	6.5%
l	8680	4.8%
s	8383	4.6%
t	5826	3.2%
m	5017	2.8%
Other values (95)	67269	36.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	182410	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	20379	11.2%
i	15063	8.3%
n	13668	7.5%
e	13659	7.5%
o	12554	6.9%
r	11912	6.5%
l	8680	4.8%
s	8383	4.6%
t	5826	3.2%
m	5017	2.8%
Other values (95)	67269	36.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	182410	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	20379	11.2%
i	15063	8.3%
n	13668	7.5%
e	13659	7.5%
o	12554	6.9%
r	11912	6.5%
l	8680	4.8%
s	8383	4.6%
t	5826	3.2%
m	5017	2.8%
Other values (95)	67269	36.9%

last_name
Text

Distinct	23789
Distinct (%)	73.0%
Missing	0
Missing (%)	0.0%
Memory size	2.1 MiB

Length

Max length	22
Median length	19
Mean length	7.1138149
Min length	2

Characters and Unicode

Total characters	231825
Distinct characters	115
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20183 ?
Unique (%)	61.9%

Sample

1st row	Klose
2nd row	Weidenfeller
3rd row	Berbatov
4th row	Lúcio
5th row	Starke

Value	Count	Frequency (%)
van	253	0.7%
de	218	0.6%
silva	99	0.3%
el	82	0.2%
garcía	71	0.2%
santos	59	0.2%
lópez	58	0.2%
rodríguez	57	0.2%
der	57	0.2%
gonzález	52	0.2%
Other values (23294)	33462	97.1%

Most occurring characters

Value	Count	Frequency (%)
a	24201	10.4%
e	19255	8.3%
o	17274	7.5%
i	16947	7.3%
n	14938	6.4%
r	14619	6.3%
l	10946	4.7%
s	10878	4.7%
u	7642	3.3%
t	7476	3.2%
Other values (105)	87649	37.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	231825	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	24201	10.4%
e	19255	8.3%
o	17274	7.5%
i	16947	7.3%
n	14938	6.4%
r	14619	6.3%
l	10946	4.7%
s	10878	4.7%
u	7642	3.3%
t	7476	3.2%
Other values (105)	87649	37.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	231825	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	24201	10.4%
e	19255	8.3%
o	17274	7.5%
i	16947	7.3%
n	14938	6.4%
r	14619	6.3%
l	10946	4.7%
s	10878	4.7%
u	7642	3.3%
t	7476	3.2%
Other values (105)	87649	37.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	231825	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	24201	10.4%
e	19255	8.3%
o	17274	7.5%
i	16947	7.3%
n	14938	6.4%
r	14619	6.3%
l	10946	4.7%
s	10878	4.7%
u	7642	3.3%
t	7476	3.2%
Other values (105)	87649	37.8%

name
Text

Distinct	31880
Distinct (%)	97.8%
Missing	0
Missing (%)	0.0%
Memory size	2.4 MiB

Length

Max length	32
Median length	29
Mean length	13.647999
Min length	2

Characters and Unicode

Total characters	444761
Distinct characters	124
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	31356 ?
Unique (%)	96.2%

Sample

1st row	Miroslav Klose
2nd row	Roman Weidenfeller
3rd row	Dimitar Berbatov
4th row	Lúcio
5th row	Tom Starke

Value	Count	Frequency (%)
van	254	0.4%
david	229	0.3%
de	218	0.3%
lucas	157	0.2%
ivan	154	0.2%
daniel	139	0.2%
marco	137	0.2%
thomas	135	0.2%
georgios	126	0.2%
aleksandr	123	0.2%
Other values (28923)	63863	97.4%

Most occurring characters

Value	Count	Frequency (%)
a	44580	10.0%
	32947	7.4%
e	32914	7.4%
i	32010	7.2%
o	29828	6.7%
n	28606	6.4%
r	26531	6.0%
l	19626	4.4%
s	19261	4.3%
t	13302	3.0%
Other values (114)	165156	37.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	444761	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	44580	10.0%
	32947	7.4%
e	32914	7.4%
i	32010	7.2%
o	29828	6.7%
n	28606	6.4%
r	26531	6.0%
l	19626	4.4%
s	19261	4.3%
t	13302	3.0%
Other values (114)	165156	37.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	444761	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	44580	10.0%
	32947	7.4%
e	32914	7.4%
i	32010	7.2%
o	29828	6.7%
n	28606	6.4%
r	26531	6.0%
l	19626	4.4%
s	19261	4.3%
t	13302	3.0%
Other values (114)	165156	37.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	444761	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	44580	10.0%
	32947	7.4%
e	32914	7.4%
i	32010	7.2%
o	29828	6.7%
n	28606	6.4%
r	26531	6.0%
l	19626	4.4%
s	19261	4.3%
t	13302	3.0%
Other values (114)	165156	37.1%

last_season
Real number (ℝ)

High correlation

Distinct	13
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2019.3561

Minimum	2012
Maximum	2024
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	2012
5-th percentile	2012
Q1	2016
median	2020
Q3	2023
95-th percentile	2024
Maximum	2024
Range	12
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	3.9620232
Coefficient of variation (CV)	0.001962023
Kurtosis	-1.1890737
Mean	2019.3561
Median Absolute Deviation (MAD)	3
Skewness	-0.40362871
Sum	65806778
Variance	15.697628
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
2024	6426	19.7%
2023	4039	12.4%
2022	2638	8.1%
2021	2336	7.2%
2018	2270	7.0%
2020	2169	6.7%
2013	2115	6.5%
2016	1918	5.9%
2017	1798	5.5%
2019	1783	5.5%
Other values (3)	5096	15.6%

Value	Count	Frequency (%)
2012	1679	5.2%
2013	2115	6.5%
2014	1704	5.2%
2015	1713	5.3%
2016	1918	5.9%
2017	1798	5.5%
2018	2270	7.0%
2019	1783	5.5%
2020	2169	6.7%
2021	2336	7.2%

Value	Count	Frequency (%)
2024	6426	19.7%
2023	4039	12.4%
2022	2638	8.1%
2021	2336	7.2%
2020	2169	6.7%
2019	1783	5.5%
2018	2270	7.0%
2017	1798	5.5%
2016	1918	5.9%
2015	1713	5.3%

current_club_id
Real number (ℝ)

Distinct	437
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4807.0534

Minimum	3
Maximum	110302
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	3
5-th percentile	41
Q1	403
median	1071
Q3	3060
95-th percentile	26459
Maximum	110302
Range	110299
Interquartile range (IQR)	2657

Descriptive statistics

Standard deviation	11560.679
Coefficient of variation (CV)	2.4049408
Kurtosis	21.835704
Mean	4807.0534
Median Absolute Deviation (MAD)	923
Skewness	4.3625782
Sum	1.5665226 × 10⁸
Variance	1.3364929 × 10⁸
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2553	185	0.6%
987	182	0.6%
2759	180	0.6%
511	178	0.5%
2578	166	0.5%
126	166	0.5%
589	164	0.5%
252	163	0.5%
800	158	0.5%
6676	156	0.5%
Other values (427)	30890	94.8%

Value	Count	Frequency (%)
3	74	0.2%
4	37	0.1%
5	93	0.3%
6	27	0.1%
10	28	0.1%
11	75	0.2%
12	123	0.4%
13	91	0.3%
15	75	0.2%
16	64	0.2%

Value	Count	Frequency (%)
110302	28	0.1%
86209	24	0.1%
85465	28	0.1%
83678	36	0.1%
75231	23	0.1%
71985	26	0.1%
68608	51	0.2%
63007	45	0.1%
61825	80	0.2%
60949	100	0.3%

player_code
Text

Distinct	31840
Distinct (%)	97.7%
Missing	0
Missing (%)	0.0%
Memory size	2.2 MiB

Length

Max length	35
Median length	30
Mean length	13.645913
Min length	4

Characters and Unicode

Total characters	444693
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	31292 ?
Unique (%)	96.0%

Sample

1st row	miroslav-klose
2nd row	roman-weidenfeller
3rd row	dimitar-berbatov
4th row	lucio
5th row	tom-starke

Value	Count	Frequency (%)
paulinho	13	< 0.1%
joao-pedro	9	< 0.1%
guilherme	8	< 0.1%
vinicius	7	< 0.1%
alex	7	< 0.1%
danilo	7	< 0.1%
bruninho	7	< 0.1%
serginho	7	< 0.1%
julio-cesar	7	< 0.1%
pedro	5	< 0.1%
Other values (31830)	32511	99.8%

Most occurring characters

Value	Count	Frequency (%)
a	51517	11.6%
e	36575	8.2%
i	34264	7.7%
-	33422	7.5%
o	32280	7.3%
n	30729	6.9%
r	29590	6.7%
s	24635	5.5%
l	22478	5.1%
m	16272	3.7%
Other values (22)	132931	29.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	444693	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	51517	11.6%
e	36575	8.2%
i	34264	7.7%
-	33422	7.5%
o	32280	7.3%
n	30729	6.9%
r	29590	6.7%
s	24635	5.5%
l	22478	5.1%
m	16272	3.7%
Other values (22)	132931	29.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	444693	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	51517	11.6%
e	36575	8.2%
i	34264	7.7%
-	33422	7.5%
o	32280	7.3%
n	30729	6.9%
r	29590	6.7%
s	24635	5.5%
l	22478	5.1%
m	16272	3.7%
Other values (22)	132931	29.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	444693	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	51517	11.6%
e	36575	8.2%
i	34264	7.7%
-	33422	7.5%
o	32280	7.3%
n	30729	6.9%
r	29590	6.7%
s	24635	5.5%
l	22478	5.1%
m	16272	3.7%
Other values (22)	132931	29.9%

country_of_birth
Text

Missing

Distinct	185
Distinct (%)	0.6%
Missing	2809
Missing (%)	8.6%
Memory size	1.9 MiB

Length

Max length	30
Median length	24
Mean length	7.2366433
Min length	4

Characters and Unicode

Total characters	215500
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	25 ?
Unique (%)	0.1%

Sample

1st row	Poland
2nd row	Germany
3rd row	Bulgaria
4th row	Brazil
5th row	East Germany (GDR)

Value	Count	Frequency (%)
france	2334	7.4%
spain	1995	6.3%
italy	1849	5.9%
england	1832	5.8%
germany	1653	5.2%
brazil	1617	5.1%
netherlands	1522	4.8%
turkey	1196	3.8%
portugal	1119	3.5%
ukraine	1020	3.2%
Other values (203)	15444	48.9%

Most occurring characters

Value	Count	Frequency (%)
a	26461	12.3%
e	20519	9.5%
n	19220	8.9%
r	16652	7.7%
l	12861	6.0%
i	12408	5.8%
t	7884	3.7%
d	6992	3.2%
u	6853	3.2%
S	6656	3.1%
Other values (50)	78994	36.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	215500	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	26461	12.3%
e	20519	9.5%
n	19220	8.9%
r	16652	7.7%
l	12861	6.0%
i	12408	5.8%
t	7884	3.7%
d	6992	3.2%
u	6853	3.2%
S	6656	3.1%
Other values (50)	78994	36.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	215500	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	26461	12.3%
e	20519	9.5%
n	19220	8.9%
r	16652	7.7%
l	12861	6.0%
i	12408	5.8%
t	7884	3.7%
d	6992	3.2%
u	6853	3.2%
S	6656	3.1%
Other values (50)	78994	36.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	215500	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	26461	12.3%
e	20519	9.5%
n	19220	8.9%
r	16652	7.7%
l	12861	6.0%
i	12408	5.8%
t	7884	3.7%
d	6992	3.2%
u	6853	3.2%
S	6656	3.1%
Other values (50)	78994	36.7%

city_of_birth
Text

Missing

Distinct	8578
Distinct (%)	28.5%
Missing	2465
Missing (%)	7.6%
Memory size	2.1 MiB

Length

Max length	56
Median length	42
Mean length	9.141387
Min length	1

Characters and Unicode

Total characters	275366
Distinct characters	151
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5270 ?
Unique (%)	17.5%

Sample

1st row	Opole
2nd row	Diez
3rd row	Blagoevgrad
4th row	Brasília
5th row	Freital

Value	Count	Frequency (%)
de	742	1.9%
oblast	711	1.8%
london	479	1.2%
istanbul	341	0.9%
são	332	0.9%
region	298	0.8%
moskau	248	0.6%
amsterdam	204	0.5%
paulo	197	0.5%
san	196	0.5%
Other values (8974)	35216	90.4%

Most occurring characters

Value	Count	Frequency (%)
a	29973	10.9%
e	22135	8.0%
o	19265	7.0%
r	18215	6.6%
n	18189	6.6%
i	17403	6.3%
s	12954	4.7%
l	12911	4.7%
t	10377	3.8%
	8841	3.2%
Other values (141)	105103	38.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	275366	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	29973	10.9%
e	22135	8.0%
o	19265	7.0%
r	18215	6.6%
n	18189	6.6%
i	17403	6.3%
s	12954	4.7%
l	12911	4.7%
t	10377	3.8%
	8841	3.2%
Other values (141)	105103	38.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	275366	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	29973	10.9%
e	22135	8.0%
o	19265	7.0%
r	18215	6.6%
n	18189	6.6%
i	17403	6.3%
s	12954	4.7%
l	12911	4.7%
t	10377	3.8%
	8841	3.2%
Other values (141)	105103	38.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	275366	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	29973	10.9%
e	22135	8.0%
o	19265	7.0%
r	18215	6.6%
n	18189	6.6%
i	17403	6.3%
s	12954	4.7%
l	12911	4.7%
t	10377	3.8%
	8841	3.2%
Other values (141)	105103	38.2%

country_of_citizenship
Text

Missing

Distinct	183
Distinct (%)	0.6%
Missing	383
Missing (%)	1.2%
Memory size	2.0 MiB

Length

Max length	24
Median length	21
Mean length	7.0979041
Min length	4

Characters and Unicode

Total characters	228588
Distinct characters	58
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	0.1%

Sample

1st row	Germany
2nd row	Germany
3rd row	Bulgaria
4th row	Brazil
5th row	Germany

Value	Count	Frequency (%)
spain	1966	5.8%
italy	1888	5.6%
france	1762	5.2%
brazil	1626	4.8%
england	1591	4.7%
ukraine	1494	4.4%
russia	1481	4.4%
netherlands	1445	4.3%
turkey	1424	4.2%
germany	1338	4.0%
Other values (202)	17677	52.5%

Most occurring characters

Value	Count	Frequency (%)
a	28918	12.7%
e	22992	10.1%
n	19666	8.6%
r	19036	8.3%
i	14782	6.5%
l	13078	5.7%
t	8418	3.7%
o	7815	3.4%
u	7602	3.3%
g	6497	2.8%
Other values (48)	79784	34.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	228588	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	28918	12.7%
e	22992	10.1%
n	19666	8.6%
r	19036	8.3%
i	14782	6.5%
l	13078	5.7%
t	8418	3.7%
o	7815	3.4%
u	7602	3.3%
g	6497	2.8%
Other values (48)	79784	34.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	228588	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	28918	12.7%
e	22992	10.1%
n	19666	8.6%
r	19036	8.3%
i	14782	6.5%
l	13078	5.7%
t	8418	3.7%
o	7815	3.4%
u	7602	3.3%
g	6497	2.8%
Other values (48)	79784	34.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	228588	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	28918	12.7%
e	22992	10.1%
n	19666	8.6%
r	19036	8.3%
i	14782	6.5%
l	13078	5.7%
t	8418	3.7%
o	7815	3.4%
u	7602	3.3%
g	6497	2.8%
Other values (48)	79784	34.9%

date_of_birth
Date

Distinct	9295
Distinct (%)	28.6%
Missing	47
Missing (%)	0.1%
Memory size	254.7 KiB

Minimum	1968-07-31 00:00:00
Maximum	2009-09-08 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=50)

sub_position
Categorical

High correlation

Distinct	13
Distinct (%)	< 0.1%
Missing	181
Missing (%)	0.6%
Memory size	2.2 MiB

Centre-Back	5746
Centre-Forward	4570
Central Midfield	3783
Goalkeeper	3723
Defensive Midfield	2657
Other values (8)	11928

Length

Max length	18
Median length	14
Mean length	12.869442
Min length	9

Characters and Unicode

Total characters	417060
Distinct characters	31
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Centre-Forward
2nd row	Goalkeeper
3rd row	Centre-Forward
4th row	Centre-Back
5th row	Goalkeeper

Common Values

Value	Count	Frequency (%)
Centre-Back	5746	17.6%
Centre-Forward	4570	14.0%
Central Midfield	3783	11.6%
Goalkeeper	3723	11.4%
Defensive Midfield	2657	8.2%
Right-Back	2371	7.3%
Left-Back	2284	7.0%
Attacking Midfield	2221	6.8%
Left Winger	2103	6.5%
Right Winger	2001	6.1%
Other values (3)	948	2.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
midfield	9371	20.3%
centre-back	5746	12.5%
centre-forward	4570	9.9%
winger	4104	8.9%
central	3783	8.2%
goalkeeper	3723	8.1%
defensive	2657	5.8%
left	2474	5.4%
right-back	2371	5.1%
right	2340	5.1%
Other values (4)	4981	10.8%

Most occurring characters

Value	Count	Frequency (%)
e	62264	14.9%
i	32673	7.8%
r	31542	7.6%
t	28248	6.8%
a	24698	5.9%
d	23550	5.6%
n	23319	5.6%
l	16877	4.0%
f	16786	4.0%
k	16583	4.0%
Other values (21)	140520	33.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	417060	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	62264	14.9%
i	32673	7.8%
r	31542	7.6%
t	28248	6.8%
a	24698	5.9%
d	23550	5.6%
n	23319	5.6%
l	16877	4.0%
f	16786	4.0%
k	16583	4.0%
Other values (21)	140520	33.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	417060	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	62264	14.9%
i	32673	7.8%
r	31542	7.6%
t	28248	6.8%
a	24698	5.9%
d	23550	5.6%
n	23319	5.6%
l	16877	4.0%
f	16786	4.0%
k	16583	4.0%
Other values (21)	140520	33.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	417060	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	62264	14.9%
i	32673	7.8%
r	31542	7.6%
t	28248	6.8%
a	24698	5.9%
d	23550	5.6%
n	23319	5.6%
l	16877	4.0%
f	16786	4.0%
k	16583	4.0%
Other values (21)	140520	33.7%

position
Categorical

High correlation

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.0 MiB

Defender	10401
Midfield	9371
Attack	8912
Goalkeeper	3723
Missing	181

Length

Max length	10
Median length	8
Mean length	7.675985
Min length	6

Characters and Unicode

Total characters	250145
Distinct characters	19
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Attack
2nd row	Goalkeeper
3rd row	Attack
4th row	Defender
5th row	Goalkeeper

Common Values

Value	Count	Frequency (%)
Defender	10401	31.9%
Midfield	9371	28.8%
Attack	8912	27.3%
Goalkeeper	3723	11.4%
Missing	181	0.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
defender	10401	31.9%
midfield	9371	28.8%
attack	8912	27.3%
goalkeeper	3723	11.4%
missing	181	0.6%

Most occurring characters

Value	Count	Frequency (%)
e	51743	20.7%
d	29143	11.7%
f	19772	7.9%
i	19104	7.6%
t	17824	7.1%
r	14124	5.6%
l	13094	5.2%
k	12635	5.1%
a	12635	5.1%
n	10582	4.2%
Other values (9)	49489	19.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	250145	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	51743	20.7%
d	29143	11.7%
f	19772	7.9%
i	19104	7.6%
t	17824	7.1%
r	14124	5.6%
l	13094	5.2%
k	12635	5.1%
a	12635	5.1%
n	10582	4.2%
Other values (9)	49489	19.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	250145	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	51743	20.7%
d	29143	11.7%
f	19772	7.9%
i	19104	7.6%
t	17824	7.1%
r	14124	5.6%
l	13094	5.2%
k	12635	5.1%
a	12635	5.1%
n	10582	4.2%
Other values (9)	49489	19.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	250145	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	51743	20.7%
d	29143	11.7%
f	19772	7.9%
i	19104	7.6%
t	17824	7.1%
r	14124	5.6%
l	13094	5.2%
k	12635	5.1%
a	12635	5.1%
n	10582	4.2%
Other values (9)	49489	19.8%

foot
Categorical

Missing

Distinct	3
Distinct (%)	< 0.1%
Missing	2540
Missing (%)	7.8%
Memory size	1.9 MiB

right	21132
left	7530
both	1386

Length

Max length	5
Median length	5
Mean length	4.7032748
Min length	4

Characters and Unicode

Total characters	141324
Distinct characters	10
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	right
2nd row	left
3rd row	right
4th row	both
5th row	right

Common Values

Value	Count	Frequency (%)
right	21132	64.8%
left	7530	23.1%
both	1386	4.3%
(Missing)	2540	7.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
right	21132	70.3%
left	7530	25.1%
both	1386	4.6%

Most occurring characters

Value	Count	Frequency (%)
t	30048	21.3%
h	22518	15.9%
r	21132	15.0%
i	21132	15.0%
g	21132	15.0%
l	7530	5.3%
e	7530	5.3%
f	7530	5.3%
b	1386	1.0%
o	1386	1.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	141324	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	30048	21.3%
h	22518	15.9%
r	21132	15.0%
i	21132	15.0%
g	21132	15.0%
l	7530	5.3%
e	7530	5.3%
f	7530	5.3%
b	1386	1.0%
o	1386	1.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	141324	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	30048	21.3%
h	22518	15.9%
r	21132	15.0%
i	21132	15.0%
g	21132	15.0%
l	7530	5.3%
e	7530	5.3%
f	7530	5.3%
b	1386	1.0%
o	1386	1.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	141324	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	30048	21.3%
h	22518	15.9%
r	21132	15.0%
i	21132	15.0%
g	21132	15.0%
l	7530	5.3%
e	7530	5.3%
f	7530	5.3%
b	1386	1.0%
o	1386	1.0%

height_in_cm
Real number (ℝ)

Missing

Distinct	52
Distinct (%)	0.2%
Missing	2263
Missing (%)	6.9%
Infinite	0
Infinite (%)	0.0%
Mean	182.29082

Minimum	17
Maximum	207
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	17
5-th percentile	171
Q1	178
median	183
Q3	187
95-th percentile	193
Maximum	207
Range	190
Interquartile range (IQR)	9

Descriptive statistics

Standard deviation	7.0350572
Coefficient of variation (CV)	0.038592494
Kurtosis	48.526418
Mean	182.29082
Median Absolute Deviation (MAD)	5
Skewness	-2.1383809
Sum	5527969
Variance	49.492029
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
180	2187	6.7%
185	2031	6.2%
178	1789	5.5%
183	1757	5.4%
188	1550	4.8%
182	1521	4.7%
184	1491	4.6%
186	1470	4.5%
187	1434	4.4%
175	1405	4.3%
Other values (42)	13690	42.0%
(Missing)	2263	6.9%

Value	Count	Frequency (%)
17	2	< 0.1%
18	2	< 0.1%
19	1	< 0.1%
159	2	< 0.1%
160	4	< 0.1%
161	3	< 0.1%
162	7	< 0.1%
163	22	0.1%
164	35	0.1%
165	77	0.2%

Value	Count	Frequency (%)
207	1	< 0.1%
206	4	< 0.1%
205	4	< 0.1%
204	8	< 0.1%
203	11	< 0.1%
202	16	< 0.1%
201	23	0.1%
200	27	0.1%
199	29	0.1%
198	106	0.3%

contract_expiration_date
Date

Missing

Distinct	118
Distinct (%)	0.6%
Missing	12098
Missing (%)	37.1%
Memory size	254.7 KiB

Minimum	2000-05-31 00:00:00
Maximum	2034-06-30 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=50)

agent_name
Text

Missing

Distinct	2880
Distinct (%)	17.4%
Missing	16052
Missing (%)	49.3%
Memory size	1.6 MiB

Length

Max length	20
Median length	14
Mean length	11.765058
Min length	2

Characters and Unicode

Total characters	194547
Distinct characters	106
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1148 ?
Unique (%)	6.9%

Sample

1st row	ASBW Sport Marketing
2nd row	Neubauer 13 GmbH
3rd row	CSKA-AS-23 Ltd.
4th row	IFM
5th row	Football Concept

Value	Count	Frequency (%)
sports	2234	7.1%
group	1025	3.3%
sport	857	2.7%
management	724	2.3%
caa	679	2.2%
agency	586	1.9%
wasserman	502	1.6%
football	482	1.5%
ltd	441	1.4%
soccer	419	1.3%
Other values (3184)	23366	74.6%

Most occurring characters

Value	Count	Frequency (%)
	15421	7.9%
o	12320	6.3%
e	11965	6.2%
r	11946	6.1%
a	11057	5.7%
t	10413	5.4%
S	9554	4.9%
n	8321	4.3%
s	7312	3.8%
l	6761	3.5%
Other values (96)	89477	46.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	194547	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	15421	7.9%
o	12320	6.3%
e	11965	6.2%
r	11946	6.1%
a	11057	5.7%
t	10413	5.4%
S	9554	4.9%
n	8321	4.3%
s	7312	3.8%
l	6761	3.5%
Other values (96)	89477	46.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	194547	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	15421	7.9%
o	12320	6.3%
e	11965	6.2%
r	11946	6.1%
a	11057	5.7%
t	10413	5.4%
S	9554	4.9%
n	8321	4.3%
s	7312	3.8%
l	6761	3.5%
Other values (96)	89477	46.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	194547	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	15421	7.9%
o	12320	6.3%
e	11965	6.2%
r	11946	6.1%
a	11057	5.7%
t	10413	5.4%
S	9554	4.9%
n	8321	4.3%
s	7312	3.8%
l	6761	3.5%
Other values (96)	89477	46.0%

image_url
URL

Distinct	26826
Distinct (%)	82.3%
Missing	0
Missing (%)	0.0%
Memory size	4.3 MiB

https://img.a.transfermarkt.technology/portrait/header/default.jpg?lm=1	5490
https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	274
https://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1	1
https://img.a.transfermarkt.technology/portrait/header/401604-1659559936.jpg?lm=1	1
https://img.a.transfermarkt.technology/portrait/header/401768-1561123096.jpg?lm=1	1
Other values (26821)	26821

Value	Count	Frequency (%)
https://img.a.transfermarkt.technology/portrait/header/default.jpg?lm=1	5490	16.8%
https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	274	0.8%
https://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401604-1659559936.jpg?lm=1	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401768-1561123096.jpg?lm=1	1	< 0.1%
https://tmssl.akamaized.net//images/portrait/header/401736-1680154630.jpg?lm=1680154665	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401732-1473155527.jpg?lm=1	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401729-1636410142.JPG?lm=1	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401717-1533289301.jpg?lm=1	1	< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401712-1537438711.jpg?lm=1	1	< 0.1%
Other values (26816)	26816	82.3%

Value	Count	Frequency (%)
https	32588	100.0%

Value	Count	Frequency (%)
img.a.transfermarkt.technology	26162	80.3%
tmssl.akamaized.net	6426	19.7%

Value	Count	Frequency (%)
/portrait/header/default.jpg	5490	16.8%
//images/portrait/header/default.jpg	274	0.8%
/portrait/header/10-1448468291.jpg	1	< 0.1%
/portrait/header/401604-1659559936.jpg	1	< 0.1%
/portrait/header/401768-1561123096.jpg	1	< 0.1%
//images/portrait/header/401736-1680154630.jpg	1	< 0.1%
/portrait/header/401732-1473155527.jpg	1	< 0.1%
/portrait/header/401729-1636410142.JPG	1	< 0.1%
/portrait/header/401717-1533289301.jpg	1	< 0.1%
/portrait/header/401712-1537438711.jpg	1	< 0.1%
Other values (26816)	26816	82.3%

Value	Count	Frequency (%)
lm=1	26162	80.3%
lm=1455618221	274	0.8%
lm=1708426123	2	< 0.1%
lm=1625740964	1	< 0.1%
lm=1723809768	1	< 0.1%
lm=1659947739	1	< 0.1%
lm=1723819820	1	< 0.1%
lm=1605555794	1	< 0.1%
lm=1711746884	1	< 0.1%
lm=1705056589	1	< 0.1%
Other values (6143)	6143	18.9%

Value	Count	Frequency (%)
	32588	100.0%

url
URL

Unique

Distinct	32588
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	3.9 MiB

https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10	1
https://www.transfermarkt.co.uk/vasilios-chatziemmanouil/profil/spieler/420921	1
https://www.transfermarkt.co.uk/keita-endo/profil/spieler/421726	1
https://www.transfermarkt.co.uk/patryk-szysz/profil/spieler/421710	1
https://www.transfermarkt.co.uk/frank-onyeka/profil/spieler/421637	1
Other values (32583)	32583

Value	Count	Frequency (%)
https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10	1	< 0.1%
https://www.transfermarkt.co.uk/vasilios-chatziemmanouil/profil/spieler/420921	1	< 0.1%
https://www.transfermarkt.co.uk/keita-endo/profil/spieler/421726	1	< 0.1%
https://www.transfermarkt.co.uk/patryk-szysz/profil/spieler/421710	1	< 0.1%
https://www.transfermarkt.co.uk/frank-onyeka/profil/spieler/421637	1	< 0.1%
https://www.transfermarkt.co.uk/samson-iyede/profil/spieler/421636	1	< 0.1%
https://www.transfermarkt.co.uk/jake-vokins/profil/spieler/421424	1	< 0.1%
https://www.transfermarkt.co.uk/omer-alper-tatlisu/profil/spieler/421319	1	< 0.1%
https://www.transfermarkt.co.uk/maksim-rudnev/profil/spieler/421284	1	< 0.1%
https://www.transfermarkt.co.uk/thomas-vasiliou/profil/spieler/421224	1	< 0.1%
Other values (32578)	32578	> 99.9%

Value	Count	Frequency (%)
https	32588	100.0%

Value	Count	Frequency (%)
www.transfermarkt.co.uk	32588	100.0%

Value	Count	Frequency (%)
/miroslav-klose/profil/spieler/10	1	< 0.1%
/vasilios-chatziemmanouil/profil/spieler/420921	1	< 0.1%
/keita-endo/profil/spieler/421726	1	< 0.1%
/patryk-szysz/profil/spieler/421710	1	< 0.1%
/frank-onyeka/profil/spieler/421637	1	< 0.1%
/samson-iyede/profil/spieler/421636	1	< 0.1%
/jake-vokins/profil/spieler/421424	1	< 0.1%
/omer-alper-tatlisu/profil/spieler/421319	1	< 0.1%
/maksim-rudnev/profil/spieler/421284	1	< 0.1%
/thomas-vasiliou/profil/spieler/421224	1	< 0.1%
Other values (32578)	32578	> 99.9%

Value	Count	Frequency (%)
	32588	100.0%

Value	Count	Frequency (%)
	32588	100.0%

current_club_domestic_competition_id
Categorical

Distinct	14
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.9 MiB

TR1	3220
IT1	3177
PO1	2660
GR1	2604
FR1	2246
Other values (9)	18681

Length

Max length	4
Median length	3
Mean length	3.0054314
Min length	2

Characters and Unicode

Total characters	97941
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	IT1
2nd row	L1
3rd row	GR1
4th row	IT1
5th row	L1

Common Values

Value	Count	Frequency (%)
TR1	3220	9.9%
IT1	3177	9.7%
PO1	2660	8.2%
GR1	2604	8.0%
FR1	2246	6.9%
NL1	2229	6.8%
ES1	2215	6.8%
BE1	2214	6.8%
GB1	2180	6.7%
RU1	2180	6.7%
Other values (4)	7663	23.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
tr1	3220	9.9%
it1	3177	9.7%
po1	2660	8.2%
gr1	2604	8.0%
fr1	2246	6.9%
nl1	2229	6.8%
es1	2215	6.8%
be1	2214	6.8%
gb1	2180	6.7%
ru1	2180	6.7%
Other values (4)	7663	23.5%

Most occurring characters

Value	Count	Frequency (%)
1	32588	33.3%
R	12302	12.6%
T	6397	6.5%
G	4784	4.9%
E	4429	4.5%
B	4394	4.5%
S	4237	4.3%
U	4232	4.3%
L	4104	4.2%
K	3766	3.8%
Other values (7)	16708	17.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	97941	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	32588	33.3%
R	12302	12.6%
T	6397	6.5%
G	4784	4.9%
E	4429	4.5%
B	4394	4.5%
S	4237	4.3%
U	4232	4.3%
L	4104	4.2%
K	3766	3.8%
Other values (7)	16708	17.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	97941	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	32588	33.3%
R	12302	12.6%
T	6397	6.5%
G	4784	4.9%
E	4429	4.5%
B	4394	4.5%
S	4237	4.3%
U	4232	4.3%
L	4104	4.2%
K	3766	3.8%
Other values (7)	16708	17.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	97941	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	32588	33.3%
R	12302	12.6%
T	6397	6.5%
G	4784	4.9%
E	4429	4.5%
B	4394	4.5%
S	4237	4.3%
U	4232	4.3%
L	4104	4.2%
K	3766	3.8%
Other values (7)	16708	17.1%

current_club_name
Text

Distinct	437
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	2.8 MiB

Length

Max length	98
Median length	40
Mean length	21.877071
Min length	4

Characters and Unicode

Total characters	712930
Distinct characters	111
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Società Sportiva Lazio S.p.A.
2nd row	Borussia Dortmund
3rd row	Panthessalonikios Athlitikos Omilos Konstantinoupoliton
4th row	Juventus Football Club
5th row	FC Bayern München

Value	Count	Frequency (%)
club	7817	7.7%
football	5227	5.1%
fc	2468	2.4%
de	2180	2.1%
kulübü	1411	1.4%
clube	1387	1.4%
fk	1321	1.3%
calcio	1256	1.2%
s.a.d	1125	1.1%
koninklijke	858	0.8%
Other values (772)	76539	75.3%

Most occurring characters

Value	Count	Frequency (%)
	69001	9.7%
o	51907	7.3%
l	49996	7.0%
e	48588	6.8%
a	48400	6.8%
i	38245	5.4%
n	34273	4.8%
r	33727	4.7%
t	33000	4.6%
b	25431	3.6%
Other values (101)	280362	39.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	712930	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	69001	9.7%
o	51907	7.3%
l	49996	7.0%
e	48588	6.8%
a	48400	6.8%
i	38245	5.4%
n	34273	4.8%
r	33727	4.7%
t	33000	4.6%
b	25431	3.6%
Other values (101)	280362	39.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	712930	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	69001	9.7%
o	51907	7.3%
l	49996	7.0%
e	48588	6.8%
a	48400	6.8%
i	38245	5.4%
n	34273	4.8%
r	33727	4.7%
t	33000	4.6%
b	25431	3.6%
Other values (101)	280362	39.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	712930	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	69001	9.7%
o	51907	7.3%
l	49996	7.0%
e	48588	6.8%
a	48400	6.8%
i	38245	5.4%
n	34273	4.8%
r	33727	4.7%
t	33000	4.6%
b	25431	3.6%
Other values (101)	280362	39.3%

market_value_in_eur
Real number (ℝ)

High correlation Missing

Distinct	128
Distinct (%)	0.4%
Missing	1564
Missing (%)	4.8%
Infinite	0
Infinite (%)	0.0%
Mean	1619112

Minimum	10000
Maximum	2 × 10⁸
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	10000
5-th percentile	25000
Q1	100000
median	250000
Q3	700000
95-th percentile	7000000
Maximum	2 × 10⁸
Range	1.9999 × 10⁸
Interquartile range (IQR)	600000

Descriptive statistics

Standard deviation	6359597.7
Coefficient of variation (CV)	3.9278307
Kurtosis	208.47248
Mean	1619112
Median Absolute Deviation (MAD)	175000
Skewness	11.400756
Sum	5.023133 × 10¹⁰
Variance	4.0444483 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
100000	3036	9.3%
50000	2900	8.9%
200000	2518	7.7%
150000	2152	6.6%
300000	1892	5.8%
250000	1750	5.4%
25000	1325	4.1%
75000	1163	3.6%
400000	1151	3.5%
500000	1132	3.5%
Other values (118)	12005	36.8%
(Missing)	1564	4.8%

Value	Count	Frequency (%)
10000	229	0.7%
20000	2	< 0.1%
25000	1325	4.1%
50000	2900	8.9%
75000	1163	3.6%
100000	3036	9.3%
125000	681	2.1%
150000	2152	6.6%
175000	486	1.5%
200000	2518	7.7%

Value	Count	Frequency (%)
200000000	2	< 0.1%
180000000	2	< 0.1%
160000000	1	< 0.1%
150000000	1	< 0.1%
140000000	3	< 0.1%
130000000	3	< 0.1%
110000000	2	< 0.1%
100000000	3	< 0.1%
90000000	1	< 0.1%
85000000	2	< 0.1%

highest_market_value_in_eur
Real number (ℝ)

High correlation Missing

Distinct	205
Distinct (%)	0.7%
Missing	1564
Missing (%)	4.8%
Infinite	0
Infinite (%)	0.0%
Mean	3736375.7

Minimum	10000
Maximum	2 × 10⁸
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	254.7 KiB

Quantile statistics

Minimum	10000
5-th percentile	50000
Q1	275000
median	800000
Q3	3000000
95-th percentile	18000000
Maximum	2 × 10⁸
Range	1.9999 × 10⁸
Interquartile range (IQR)	2725000

Descriptive statistics

Standard deviation	9784711.2
Coefficient of variation (CV)	2.6187707
Kurtosis	76.841006
Mean	3736375.7
Median Absolute Deviation (MAD)	675000
Skewness	7.0757113
Sum	1.1591732 × 10¹¹
Variance	9.5740572 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
300000	1386	4.3%
100000	1380	4.2%
50000	1377	4.2%
1000000	1352	4.1%
500000	1324	4.1%
200000	1288	4.0%
400000	1250	3.8%
150000	1115	3.4%
1500000	1114	3.4%
2000000	1060	3.3%
Other values (195)	18378	56.4%
(Missing)	1564	4.8%

Value	Count	Frequency (%)
10000	44	0.1%
25000	318	1.0%
50000	1377	4.2%
75000	512	1.6%
100000	1380	4.2%
125000	289	0.9%
150000	1115	3.4%
175000	225	0.7%
200000	1288	4.0%
225000	146	0.4%

Value	Count	Frequency (%)
200000000	3	< 0.1%
180000000	4	< 0.1%
160000000	1	< 0.1%
150000000	9	< 0.1%
140000000	2	< 0.1%
130000000	4	< 0.1%
120000000	4	< 0.1%
110000000	5	< 0.1%
100000000	15	< 0.1%
90000000	20	0.1%

Interactions

Correlations

	current_club_domestic_competition_id	current_club_id	foot	height_in_cm	highest_market_value_in_eur	last_season	market_value_in_eur	player_id	position	sub_position
current_club_domestic_competition_id	1.000	0.220	0.079	0.054	0.083	0.043	0.071	0.070	0.041	0.051
current_club_id	0.220	1.000	0.009	-0.035	-0.185	-0.037	-0.194	0.024	0.012	0.006
foot	0.079	0.009	1.000	0.046	0.000	0.043	0.000	0.040	0.125	0.355
height_in_cm	0.054	-0.035	0.046	1.000	-0.025	0.073	-0.008	0.015	0.234	0.297
highest_market_value_in_eur	0.083	-0.185	0.000	-0.025	1.000	0.254	0.692	-0.352	0.031	0.023
last_season	0.043	-0.037	0.043	0.073	0.254	1.000	0.526	0.563	0.034	0.027
market_value_in_eur	0.071	-0.194	0.000	-0.008	0.692	0.526	1.000	0.142	0.017	0.011
player_id	0.070	0.024	0.040	0.015	-0.352	0.563	0.142	1.000	0.025	0.033
position	0.041	0.012	0.125	0.234	0.031	0.034	0.017	0.025	1.000	1.000
sub_position	0.051	0.006	0.355	0.297	0.023	0.027	0.011	0.033	1.000	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	date_of_birth	sub_position	position	foot	height_in_cm	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur
0	10	Miroslav	Klose	Miroslav Klose	2015	398	miroslav-klose	Poland	Opole	Germany	1978-06-09 00:00:00	Centre-Forward	Attack	right	184.0	NaN	ASBW Sport Marketing	https://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1	https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10	IT1	Società Sportiva Lazio S.p.A.	1000000.0	30000000.0
1	26	Roman	Weidenfeller	Roman Weidenfeller	2017	16	roman-weidenfeller	Germany	Diez	Germany	1980-08-06 00:00:00	Goalkeeper	Goalkeeper	left	190.0	NaN	Neubauer 13 GmbH	https://img.a.transfermarkt.technology/portrait/header/26-1502448725.jpg?lm=1	https://www.transfermarkt.co.uk/roman-weidenfeller/profil/spieler/26	L1	Borussia Dortmund	750000.0	8000000.0
2	65	Dimitar	Berbatov	Dimitar Berbatov	2015	1091	dimitar-berbatov	Bulgaria	Blagoevgrad	Bulgaria	1981-01-30 00:00:00	Centre-Forward	Attack	NaN	NaN	NaN	CSKA-AS-23 Ltd.	https://img.a.transfermarkt.technology/portrait/header/65-1683670068.jpg?lm=1	https://www.transfermarkt.co.uk/dimitar-berbatov/profil/spieler/65	GR1	Panthessalonikios Athlitikos Omilos Konstantinoupoliton	1000000.0	34500000.0
3	77	NaN	Lúcio	Lúcio	2012	506	lucio	Brazil	Brasília	Brazil	1978-05-08 00:00:00	Centre-Back	Defender	NaN	NaN	NaN	NaN	https://img.a.transfermarkt.technology/portrait/header/77-1458201664.jpg?lm=1	https://www.transfermarkt.co.uk/lucio/profil/spieler/77	IT1	Juventus Football Club	200000.0	24500000.0
4	80	Tom	Starke	Tom Starke	2017	27	tom-starke	East Germany (GDR)	Freital	Germany	1981-03-18 00:00:00	Goalkeeper	Goalkeeper	right	194.0	NaN	IFM	https://img.a.transfermarkt.technology/portrait/header/80-1471439914.JPG?lm=1	https://www.transfermarkt.co.uk/tom-starke/profil/spieler/80	L1	FC Bayern München	100000.0	3000000.0
5	109	NaN	Dedê	Dedê	2013	825	dede	Brazil	Belo Horizonte	Brazil	1978-04-18 00:00:00	Left-Back	Defender	NaN	NaN	NaN	Football Concept	https://img.a.transfermarkt.technology/portrait/header/109-1487263703.jpg?lm=1	https://www.transfermarkt.co.uk/dede/profil/spieler/109	TR1	Eskisehirspor	400000.0	9500000.0
6	123	Christoph	Metzelder	Christoph Metzelder	2012	33	christoph-metzelder	Germany	Haltern	Germany	1980-11-05 00:00:00	Centre-Back	Defender	NaN	NaN	NaN	NaN	https://img.a.transfermarkt.technology/portrait/header/s_123_33_2012_1.jpg?lm=1	https://www.transfermarkt.co.uk/christoph-metzelder/profil/spieler/123	L1	FC Schalke 04	1500000.0	9500000.0
7	132	Tomas	Rosicky	Tomas Rosicky	2015	11	tomas-rosicky	CSSR	Praha	Czech Republic	1980-10-04 00:00:00	Attacking Midfield	Midfield	both	179.0	NaN	NaN	https://img.a.transfermarkt.technology/portrait/header/132-1464947592.jpg?lm=1	https://www.transfermarkt.co.uk/tomas-rosicky/profil/spieler/132	GB1	Arsenal Football Club	350000.0	17500000.0
8	162	Marc	Ziegler	Marc Ziegler	2012	79	marc-ziegler	Germany	Blieskastel	Germany	1976-06-13 00:00:00	Goalkeeper	Goalkeeper	right	193.0	NaN	NaN	https://img.a.transfermarkt.technology/portrait/header/s_162_79_2012_1.jpg?lm=1	https://www.transfermarkt.co.uk/marc-ziegler/profil/spieler/162	L1	Verein für Bewegungsspiele Stuttgart 1893	200000.0	1250000.0
9	215	Roque	Santa Cruz	Roque Santa Cruz	2015	1084	roque-santa-cruz	Paraguay	Asunción	Paraguay	1981-08-16 00:00:00	Centre-Forward	Attack	right	193.0	2023-12-31 00:00:00	NaN	https://img.a.transfermarkt.technology/portrait/header/215-1564992591.png?lm=1	https://www.transfermarkt.co.uk/roque-santa-cruz/profil/spieler/215	ES1	Málaga CF	250000.0	12000000.0

	player_id	first_name	last_name	name	last_season	current_club_id	player_code	country_of_birth	city_of_birth	country_of_citizenship	date_of_birth	sub_position	position	foot	height_in_cm	contract_expiration_date	agent_name	image_url	url	current_club_domestic_competition_id	current_club_name	market_value_in_eur	highest_market_value_in_eur
32578	1358447	Kyan	Himpe	Kyan Himpe	2024	601	kyan-himpe	NaN	NaN	Belgium	2006-04-03 00:00:00	Centre-Forward	Attack	NaN	NaN	2027-06-30 00:00:00	NaN	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/kyan-himpe/profil/spieler/1358447	BE1	Koninklijke Voetbalclub Kortrijk	NaN	NaN
32579	1358448	Fuhna	Wirsiy Nsolo	Fuhna Wirsiy Nsolo	2024	601	fuhna-wirsiy-nsolo	NaN	NaN	Belgium	2005-04-26 00:00:00	Centre-Back	Defender	NaN	NaN	2027-06-30 00:00:00	NaN	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/fuhna-wirsiy-nsolo/profil/spieler/1358448	BE1	Koninklijke Voetbalclub Kortrijk	NaN	NaN
32580	1364960	Darryl	Nkulikiyimana	Darryl Nkulikiyimana	2024	9010	darryl-nkulikiyimana	NaN	NaN	Belgium	2005-05-24 00:00:00	Centre-Back	Defender	left	NaN	NaN	NaN	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/darryl-nkulikiyimana/profil/spieler/1364960	BE1	FC Verbroedering Denderhoutem Denderleeuw Eendracht Hekelgem	NaN	NaN
32581	1365020	Afonso	Assis	Afonso Assis	2024	979	afonso-assis	NaN	NaN	Portugal	2006-07-15 00:00:00	Defensive Midfield	Midfield	left	NaN	2025-06-30 00:00:00	NaN	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/afonso-assis/profil/spieler/1365020	PO1	Moreirense Futebol Clube	NaN	NaN
32582	1367128	Abdoulaye	Yoro	Abdoulaye Yoro	2024	6890	abdoulaye-yoro	NaN	NaN	Cote d'Ivoire	2006-12-12 00:00:00	Attacking Midfield	Midfield	left	175.0	2027-06-30 00:00:00	Tamegnon Consulting	https://tmssl.akamaized.net//images/portrait/header/1367128-1739874937.png?lm=1739875017	https://www.transfermarkt.co.uk/abdoulaye-yoro/profil/spieler/1367128	TR1	İstanbul Başakşehir Futbol Kulübü	NaN	NaN
32583	1369057	Yusuf	Kurt	Yusuf Kurt	2024	11282	yusuf-kurt	Türkiye	Batman	Türkiye	2002-05-07 00:00:00	Attacking Midfield	Midfield	left	186.0	2025-06-30 00:00:00	NaN	https://tmssl.akamaized.net//images/portrait/header/1369057-1741084226.jpg?lm=1741084233	https://www.transfermarkt.co.uk/yusuf-kurt/profil/spieler/1369057	TR1	Alanyaspor	NaN	NaN
32584	1375876	NaN	Diego Henrique	Diego Henrique	2024	86209	diego-henrique	Brazil	Andradina, SP	Brazil	2003-08-23 00:00:00	Attacking Midfield	Midfield	left	170.0	2026-06-30 00:00:00	NaN	https://tmssl.akamaized.net//images/portrait/header/1375876-1739627135.png?lm=1739627187	https://www.transfermarkt.co.uk/diego-henrique/profil/spieler/1375876	UKR1	FK Livyi Bereh	NaN	NaN
32585	1378362	Orseer	Achihi	Orseer Achihi	2024	1096	orseer-achihi	NaN	NaN	Nigeria	2006-09-19 00:00:00	Left Winger	Attack	NaN	NaN	2029-06-30 00:00:00	Aneke/PMG	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/orseer-achihi/profil/spieler/1378362	BE1	Royal Antwerp Football Club	NaN	NaN
32586	1380311	Prince Amoako	Junior	Prince Amoako Junior	2024	2778	prince-amoako-junior	NaN	NaN	Ghana	2007-02-19 00:00:00	Left Winger	Attack	NaN	NaN	2029-12-31 00:00:00	CAA Stellar	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/prince-amoako-junior/profil/spieler/1380311	DK1	Fodbold Club Nordsjælland	NaN	NaN
32587	1380876	Gabriel Jesus	David	Gabriel Jesus David	2024	1096	gabriel-jesus-david	NaN	NaN	Nigeria	2007-01-02 00:00:00	Centre-Forward	Attack	NaN	NaN	2025-06-30 00:00:00	Aneke/PMG	https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221	https://www.transfermarkt.co.uk/gabriel-jesus-david/profil/spieler/1380876	BE1	Royal Antwerp Football Club	NaN	NaN

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script